gene-expr-clustering-Seurat.R
This R Markdown Notebook
reproduces the analysis of TE001 at the gene expression level. The first
section of the Tutorial makes use of the default parameters of the
Seurat pipeline. The second part of the tutorial finds a
close-to-optimal clustering solution obtained with the Louvain
algorithm, by returning the solution with the highest silhouette by
optimizing over two parameters, the number of nearest neighbors
(NN) and the resolution (res).
1. Setup directories and user-defined parameters
Clean the environment.
rm(list = ls()) Set paths to the folders containing data, metadata, output figures and saving directory (to save)
DataFolder <- "/Volumes/ac_lab_scratch/lz2841/ics-rebuttal/TE001/filtered_feature_bc_matrix/"
metadata_path <- "/Volumes/ac_lab_scratch/lz2841/ics-rebuttal/TE001-h5ad/"
savingsFolder <- DataFolder
figures_path = "/Volumes/ac_lab_scratch/lz2841/ics-rebuttal/figures/TE001_gExpr_clustering/"Decide whether to optimize the clustering solution (later on, in this markdown document) or whether to use previously optimized clustering parameters.
running_clustering <- FALSE # do not re-run clustering
cat("'runnig_clustering' is set to: ", running_clustering,"\n")'runnig_clustering' is set to: FALSE Define previously determined clustering parameters (not used if
running_clustering is set to FALSE). These
parameters were obtained with a iterative subclustering routine, as the
one shown in the following sections using the acdc package
available at: https://github.com/califano-lab/acdc. While the
reproducibility of the obtain solution was tested, future updates of the
package or different laptop configuration might result in different
solutions. If this is the case, we recommend: (1) loading the Seurat
object generated with this tutorial or (2) re-running the clustering
optimization from the scratch, by setting
running_clustering = TRUE. For enhanced performance,
acdc comes also with a Grid Search-based optimization that
allows for multi-thread, thus speeding up the computation.
# optimal clustering parameters - previously determined
# step 1
opt_param <- c(0.03013859, 22) # res NN
# step 2 (subclustering)
optim_par_list <- matrix(c(0.05655232, 7, 0.0876204, 8, 0.06659604, 7),byrow=TRUE,nrow=3,ncol=2)
colnames(optim_par_list) <- c("res", "NN")
rownames(optim_par_list) <- c("1", "3", "2")Load libraries.
library(dplyr)
library(matrixStats)
library(Seurat)
library(tibble)
library(acdc)
library(cluster)
library(ggplot2)
library(factoextra)
library(ComplexHeatmap)
library(RColorBrewer)
library(circlize)
library(gridExtra)2. Load data, perform quality control and cell filtering
Define thresholds for cell filtering.
min_cells <- 5
min_feats <- 1000
min_nFeature_RNA <- 1500
max_nFeature_RNA <- 20000
max_nCount_RNA <- 100000
mt_percent_threshold <- 10Load UMI counts and create Seurat Object.
# counts
TE001_data <- Read10X(file.path(data.dir=file.path(DataFolder))) # load counts
# generate Seurat object
TE001 <- CreateSeuratObject(counts=TE001_data,
project="TE001",
assay="RNA",
min.cells = min_cells,
min.features = min_feats)
TE001@meta.data$cell_id <- rownames(TE001@meta.data)
TE001@meta.data <- TE001@meta.data %>% dplyr::select(.,cell_id, everything())Visualize QC as violin plots before filtering.
TE001[["percent.mt"]] <- PercentageFeatureSet(TE001, pattern="^mt")
VlnPlot(TE001, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size=0)
Filter out low quality cells and visualize QC plots after filtering.
# Filter out cells
TE001 <- subset(TE001, subset = nFeature_RNA > min_nFeature_RNA & nFeature_RNA < max_nFeature_RNA & nCount_RNA < max_nCount_RNA & percent.mt < mt_percent_threshold)
# Visualize QC metrics as a violin plot AFTER filtering
VlnPlot(TE001, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size=0)
# FeatureScatter is typically used to visualize feature-feature relationships
plot1 <- FeatureScatter(TE001, feature1 = "nCount_RNA", feature2 = "percent.mt", col="darkgray")
plot2 <- FeatureScatter(TE001, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", col="darkgray")
grid.arrange(plot1, plot2, ncol = 2)
Load additional metadata and include in Seurat object.
# load additional metadata for the cells
metadata_csv <- file.path(metadata_path, "TE001-metadata-umap-and-clusters-for-paper.csv")
metadata <- read.csv(metadata_csv)
# join metadata to Seurat object
TE001@meta.data <- TE001@meta.data %>% dplyr::left_join(., metadata,
by = c("cell_id" = "cell_id"))
rownames(TE001@meta.data) <- TE001@meta.data$cell_id3. Analysis of TE001 with standard Seurat workflow and clustering with default parameters
Log-Normalize data, find and display highly variable genes and scale data.
# Normalize Data
TE001 <- NormalizeData(TE001, normalization.method = "LogNormalize", scale.factor = 10000)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
# Finding Variable features
TE001 <- FindVariableFeatures(TE001, selection.method = "vst", nfeatures = 2000)Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|top10_hvg <- head(VariableFeatures(TE001), 10)
plot1 <- VariableFeaturePlot(TE001)
plot2 <- LabelPoints(plot = plot1, points = top10_hvg, repel = TRUE, xnudge=0, ynudge=0)
plot2
dev.off()null device
1
# Scaling the data
all.genes <- rownames(TE001)
TE001 <- ScaleData(TE001, features = all.genes)Centering and scaling data matrix
|
| | 0%
|
|========= | 6%
|
|================== | 12%
|
|=========================== | 19%
|
|==================================== | 25%
|
|============================================ | 31%
|
|===================================================== | 38%
|
|============================================================== | 44%
|
|======================================================================= | 50%
|
|================================================================================ | 56%
|
|========================================================================================= | 62%
|
|================================================================================================== | 69%
|
|========================================================================================================== | 75%
|
|=================================================================================================================== | 81%
|
|============================================================================================================================ | 88%
|
|===================================================================================================================================== | 94%
|
|==============================================================================================================================================| 100%Cluster cells with Louvain algorithm. 0.5 is used as the resolution value, as in https://satijalab.org/seurat/articles/pbmc3k_tutorial.
# Perform linear dimensionality reduction
TE001 <- RunPCA(TE001, verbose=F)
# Cluster the cells with default parameters
TE001 <- FindNeighbors(TE001, k.param = 20)Computing nearest neighbor graph
Computing SNNTE001 <- FindClusters(TE001, resolution = 0.5) #Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3656
Number of edges: 103035
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8939
Number of communities: 12
Elapsed time: 0 seconds
levels(TE001@meta.data$seurat_clusters) <- as.character(as.numeric(levels(TE001@meta.data$seurat_clusters)) + 1)Evaluate the quality of clustering solution by visualizing the Silhouette Score.
# evaluate clustering solution
PCs<-as.data.frame(TE001$pca@cell.embeddings[,1:10])
euc_distance<-dist(PCs, method="euclidean")
s <- silhouette( as.integer(TE001$seurat_clusters) , euc_distance )
pdf(file.path(figures_path, "silhouette_default.pdf") , width = 6 , height = 8)
fviz_silhouette(s,print.summary = FALSE)
dev.off()null device
1 fviz_silhouette(s,print.summary = FALSE)
Visualize clusters at gene expression on a UMAP embedding.
TE001 <- RunUMAP(TE001, dims = 1:10)10:15:43 UMAP embedding parameters a = 0.9922 b = 1.112
10:15:43 Read 3656 rows and found 10 numeric columns
10:15:43 Using Annoy for neighbor search, n_neighbors = 30
10:15:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:15:43 Writing NN index file to temp file /var/folders/_d/34rjbm053_n2jgnf4njw2l7h0000gn/T//Rtmp99Ud1h/file1b56732c8ea8
10:15:43 Searching Annoy index using 1 thread, search_k = 3000
10:15:44 Annoy recall = 100%
10:15:44 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
10:15:45 Initializing from normalized Laplacian + noise (using irlba)
10:15:45 Commencing optimization for 500 epochs, with 135708 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:15:48 Optimization finished
pdf(file.path(figures_path, "umap_clusters_default.pdf") ,width = 12, height = 6)
DimPlot(TE001, reduction = "umap", pt.size=1.5, group.by = c("seurat_clusters","iter_cluster_id_with_paneth"))
dev.off()null device
1
pdf(file.path(figures_path, "umap_default_cytotrace.pdf") , width = 6 , height = 6)FeaturePlot(TE001, features = "cytotrace_te001", pt.size=1.5) & scale_color_viridis_c()Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.dev.off()null device
1 # Show figures in cell
DimPlot(TE001, reduction = "umap", pt.size=1.5, group.by = "seurat_clusters")
DimPlot(TE001, reduction = "umap", pt.size=1.5, group.by = "iter_cluster_id_with_paneth")
FeaturePlot(TE001, features = "cytotrace_te001", pt.size=1.5) & scale_color_viridis_c()Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Perform differential gene expression analysis on the identified clusters with default Seurat pipeline
Idents(TE001) <- "seurat_clusters"
# FindAllMarkers with default parameters
TE001_markers <- FindAllMarkers(TE001,
assay = "RNA",
slot="data",
test.use = "wilcox",
only.pos = TRUE)Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 10% ~02s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 5 % ~01s
|+++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 33% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|+++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 3 % ~01s
|++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 14% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 25% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 3 % ~01s
|+++ | 4 % ~01s
|+++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~00s
|++++++++++++++++++++ | 39% ~00s
|+++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|++++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|+++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 8 % ~02s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|+++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 4 % ~03s
|+++ | 6 % ~03s
|++++ | 7 % ~03s
|++++ | 8 % ~03s
|+++++ | 9 % ~02s
|++++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 13% ~02s
|++++++++ | 15% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 33% ~02s
|+++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~06s
|++ | 2 % ~05s
|++ | 3 % ~05s
|+++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 7 % ~05s
|+++++ | 8 % ~05s
|+++++ | 9 % ~05s
|++++++ | 10% ~05s
|++++++ | 11% ~05s
|+++++++ | 12% ~05s
|+++++++ | 14% ~05s
|++++++++ | 15% ~05s
|++++++++ | 16% ~05s
|+++++++++ | 17% ~05s
|+++++++++ | 18% ~04s
|++++++++++ | 19% ~04s
|++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|+++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|++++++++++++ | 24% ~04s
|+++++++++++++ | 25% ~04s
|++++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|+++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|++++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|+++++++++++++++++ | 32% ~04s
|+++++++++++++++++ | 33% ~04s
|++++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|+++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|+++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|++++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s Calculating cluster 9
| | 0 % ~calculating
|++ | 2 % ~00s
|+++ | 5 % ~00s
|++++ | 7 % ~00s
|+++++ | 10% ~00s
|++++++ | 12% ~00s
|++++++++ | 14% ~00s
|+++++++++ | 17% ~00s
|++++++++++ | 19% ~00s
|+++++++++++ | 21% ~00s
|++++++++++++ | 24% ~00s
|++++++++++++++ | 26% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++ | 52% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~07s
|++ | 3 % ~07s
|+++ | 4 % ~07s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 9 % ~07s
|+++++ | 10% ~07s
|++++++ | 11% ~07s
|++++++ | 12% ~07s
|+++++++ | 13% ~07s
|+++++++ | 14% ~07s
|++++++++ | 15% ~06s
|++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|+++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 24% ~06s
|+++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~05s
|+++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 43% ~04s
|++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|+++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|+++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|++++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s Calculating cluster 11
| | 0 % ~calculating
|+ | 1 % ~20s
|++ | 2 % ~20s
|++ | 3 % ~19s
|+++ | 4 % ~19s
|+++ | 5 % ~19s
|++++ | 6 % ~19s
|++++ | 7 % ~19s
|+++++ | 8 % ~19s
|+++++ | 9 % ~18s
|++++++ | 10% ~18s
|++++++ | 11% ~18s
|+++++++ | 12% ~18s
|+++++++ | 13% ~18s
|++++++++ | 14% ~17s
|++++++++ | 15% ~17s
|+++++++++ | 16% ~17s
|+++++++++ | 18% ~17s
|++++++++++ | 19% ~16s
|++++++++++ | 20% ~16s
|+++++++++++ | 21% ~16s
|+++++++++++ | 22% ~16s
|++++++++++++ | 23% ~16s
|++++++++++++ | 24% ~15s
|+++++++++++++ | 25% ~15s
|+++++++++++++ | 26% ~15s
|++++++++++++++ | 27% ~15s
|++++++++++++++ | 28% ~15s
|+++++++++++++++ | 29% ~14s
|+++++++++++++++ | 30% ~14s
|++++++++++++++++ | 31% ~14s
|++++++++++++++++ | 32% ~14s
|+++++++++++++++++ | 33% ~14s
|++++++++++++++++++ | 34% ~13s
|++++++++++++++++++ | 35% ~13s
|+++++++++++++++++++ | 36% ~13s
|+++++++++++++++++++ | 37% ~13s
|++++++++++++++++++++ | 38% ~13s
|++++++++++++++++++++ | 39% ~12s
|+++++++++++++++++++++ | 40% ~12s
|+++++++++++++++++++++ | 41% ~12s
|++++++++++++++++++++++ | 42% ~12s
|++++++++++++++++++++++ | 43% ~12s
|+++++++++++++++++++++++ | 44% ~11s
|+++++++++++++++++++++++ | 45% ~11s
|++++++++++++++++++++++++ | 46% ~11s
|++++++++++++++++++++++++ | 47% ~11s
|+++++++++++++++++++++++++ | 48% ~11s
|+++++++++++++++++++++++++ | 49% ~10s
|++++++++++++++++++++++++++ | 51% ~10s
|++++++++++++++++++++++++++ | 52% ~10s
|+++++++++++++++++++++++++++ | 53% ~10s
|+++++++++++++++++++++++++++ | 54% ~09s
|++++++++++++++++++++++++++++ | 55% ~09s
|++++++++++++++++++++++++++++ | 56% ~09s
|+++++++++++++++++++++++++++++ | 57% ~09s
|+++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~08s
|++++++++++++++++++++++++++++++ | 60% ~08s
|+++++++++++++++++++++++++++++++ | 61% ~08s
|+++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|++++++++++++++++++++++++++++++++ | 64% ~07s
|+++++++++++++++++++++++++++++++++ | 65% ~07s
|+++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|+++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~06s
|++++++++++++++++++++++++++++++++++++ | 70% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|+++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|++++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=20s Calculating cluster 12
| | 0 % ~calculating
|+ | 1 % ~10s
|+ | 2 % ~10s
|++ | 3 % ~09s
|++ | 4 % ~09s
|+++ | 5 % ~09s
|+++ | 6 % ~09s
|++++ | 7 % ~09s
|++++ | 8 % ~09s
|+++++ | 9 % ~09s
|+++++ | 10% ~08s
|++++++ | 11% ~08s
|++++++ | 12% ~08s
|+++++++ | 13% ~08s
|+++++++ | 14% ~08s
|++++++++ | 15% ~08s
|++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|+++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|++++++++++ | 20% ~08s
|+++++++++++ | 21% ~07s
|+++++++++++ | 22% ~07s
|++++++++++++ | 23% ~07s
|++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|+++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|+++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|++++++++++++++++ | 32% ~07s
|+++++++++++++++++ | 33% ~06s
|+++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~06s
|++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 37% ~06s
|+++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|+++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~05s
|++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|+++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|+++++++++++++++++++++++++ | 50% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|+++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|+++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s
top10_markers <- TE001_markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
genes_default <- top10_markers$geneGenerate a heatmap showing the top 10 differentially expressed genes on a per-cluster basis
# Visualize clusters and top DE genes on a heatmap
mat_to_show_default <- as.matrix(TE001@assays$RNA@data[genes_default,])
Clusters <- TE001$seurat_clusters
colors <- rainbow(nlevels(Clusters))
names(colors) <- levels(Clusters)
# prepare df_annot.df
per.cell.sil.width <- s[,"sil_width"]
names(per.cell.sil.width) <- colnames(TE001)
df_annot.df <- TE001@meta.data %>%
dplyr::select(cluster_id=seurat_clusters, cytotrace_score = cytotrace_te001) %>%
tibble::rownames_to_column(.,var="cell_id")
df_annot.df$cluster_id <- factor(as.numeric(df_annot.df$cluster_id))
x <- as.data.frame(per.cell.sil.width) %>%
tibble::rownames_to_column(.,var="sample_id")
df_annot.df <- left_join(df_annot.df,x,by=c("cell_id"="sample_id"))
sample_ordering_to_print <- rev( order( df_annot.df$per.cell.sil.width ) )
df_annot.df <- df_annot.df[sample_ordering_to_print,]
mat_to_show_default <- mat_to_show_default[,match(df_annot.df$cell_id,colnames(mat_to_show_default))]
stopifnot(identical( df_annot.df$cell_id, colnames(mat_to_show_default) ))
# the silhouette score
sil.fill <- rep(NA, nrow(df_annot.df))
unique_clusters <- unique(df_annot.df$cluster_id)
n_colors <- length(colors)
color_index <- 1
for (i in seq_along(unique_clusters)) {
cluster <- unique_clusters[i]
sil.fill[df_annot.df$cluster_id == cluster] <- colors[names(colors) == cluster]
color_index <- i + 1 # Use modulo to cycle through colors
}
df_annot_cols_cluster_score <- HeatmapAnnotation(
silhouette_score = anno_barplot(
df_annot.df$per.cell.sil.width,
bar_width = 1,
gp = gpar(col = NA,
fontsize = 6 ,
cex = 0.8 ,
fill = sil.fill
),
border = FALSE,
height = unit(1.25, "cm") ,
width = unit(0.5, "cm")
)
)
# heatmap annotation
df_annot.df <- df_annot.df %>% dplyr::select(cluster_id)
cluster_colors <- colors[ !is.na(names(colors)) ]
df_annot_cols = HeatmapAnnotation( df = df_annot.df,
col = list(cluster_id=cluster_colors) ,
show_legend = TRUE , na_col = "gray95",
annotation_name_gp = gpar(fontsize=0.1)
)
top10_default_hm <- Heatmap( mat_to_show_default,
col = circlize::colorRamp2(range(mat_to_show_default)/2, c("darkslateblue", "yellow")),
use_raster = FALSE ,
column_split = df_annot.df$cluster_id,
heatmap_legend_param = list(color_bar = "continuous") ,
cluster_columns = FALSE ,
cluster_rows = FALSE ,
show_column_names = FALSE ,
name = "Gene Expression (log1p)" ,
row_names_gp = gpar(fontsize = 10) , column_names_gp = gpar(fontsize=8),
row_title_side = "left" , row_title_gp = gpar(fontsize = 10, fontface="bold")
)
ht_top10_list = df_annot_cols_cluster_score %v% df_annot_cols %v% top10_default_hm
pdf(file.path(figures_path,"top_10_default_heatmap.pdf"), width = 15, height = 23)
p <- draw(ht_top10_list, column_km = 1 ,
heatmap_legend_side = "right",
annotation_legend_side = "bottom" )
dev.off()null device
1 p
Display a dotplot with the top 5 genes per cluster (using the
data slot).
top5_markers <- TE001_markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_log2FC)
genes_dp_default <- top5_markers$gene %>% unique()
# intialize lists containing dataframes
Avgs_default <- vector( mode="list", length=length(levels(TE001$seurat_clusters)) )
names(Avgs_default) <- levels(TE001$seurat_clusters)
Pct_default <- vector( mode="list", length=length(levels(TE001$seurat_clusters)) )
names(Pct_default) <- levels(TE001$seurat_clusters)
for (cluster in levels(TE001$seurat_clusters)){
cells_in_cluster <- TE001$seurat_clusters == cluster
# Avg_default
Avgs_default[[cluster]] <- TE001[["RNA"]]@data[genes_dp_default, cells_in_cluster] %>%
rowMeans() %>%
as.data.frame() %>%
rownames_to_column() %>%
dplyr::rename(.,"Gene"=rowname,"mean"=.) %>%
dplyr::mutate(cluster=cluster)
# Pct
Pct_default[[cluster]] <- TE001[["RNA"]]@data[genes_dp_default, cells_in_cluster] %>%
apply(.,1, function(x){ sum(x>0)/length(x)*100 }) %>%
as.data.frame() %>%
rownames_to_column() %>%
dplyr::rename(.,"Gene"=rowname,"Pct"=.) %>%
dplyr::mutate(cluster=cluster)
}
# Mean
df_mean.GEx_default <- do.call(rbind, Avgs_default)
rownames(df_mean.GEx_default) <- NULL
# Pct
df_pct.GEx_default <- do.call(rbind, Pct_default)
rownames(df_pct.GEx_default) <- NULL
# Summary
summary.GEx_default <- cbind(df_mean.GEx_default[,1:2],df_pct.GEx_default[,2:3])
summary.GEx_default$Gene <- factor(summary.GEx_default$Gene, levels=genes_dp_default, ordered = TRUE)
# Printing dotplot for genes
cat("Printing dotplots..\n")Printing dotplots..GEx.DGE_DotPlot_default <- ggplot(summary.GEx_default, aes(x=Gene,y=cluster)) +
geom_point(aes(size = Pct, fill= mean), color="black", shape=21) +
scale_size("% detected", range = c(0,15), breaks=c(0,25,50,75,100), limits=c(0,100)) +
scale_fill_gradient2(low="steelblue1", high = "red", mid="white",midpoint=0,
limits=range(summary.GEx_default$mean),
guide = guide_colorbar(ticks.colour = "black",
frame.colour = "black"),
name = "Average Expression\nlog1p(counts)") +
ylab("Cluster") + xlab("") +
theme_bw() +
theme(axis.text.x = element_text(size=10, angle=45, hjust=1, color="black"),
axis.text.y = element_text(size=12, color="black"),
axis.title = element_text(size=14) ,
panel.grid.major = element_blank(), panel.grid.minor = element_blank()
)
pdf(file.path(figures_path, "GEx-DGE-dotplot_default.pdf") , width = 25 , height = 9)
print(GEx.DGE_DotPlot_default)
dev.off()null device
1
print(GEx.DGE_DotPlot_default)
4. Analysis of TE001 with optimized clustering solution
Scale data with SCTransform and compute PCA.
# new PCA and UMAP will be computed
TE001@reductions$pca <- NULL
TE001@reductions$umap <- NULL
TE001 <- SCTransform(object=TE001,
assay="RNA",
ncells=ncol(TE001),
variable.features.n = length(all.genes),
do.center = TRUE,
do.scale = TRUE,
conserve.memory = TRUE)Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 15224 by 3656
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 3656 cells
|
| | 0%
|
|==================================== | 25%
|
|======================================================================= | 50%
|
|========================================================================================================== | 75%
|
|==============================================================================================================================================| 100%Found 113 outliers - those will be ignored in fitting/regularization step
Skip calculation of full residual matrix
Will not return corrected UMI because residual type is not set to 'pearson'
Calculating gene attributes
Wall clock passed: Time difference of 13.81408 secs
Setting min_variance to: -Inf
Calculating variance for residuals of type pearson for 15224 genes
|
| | 0%
|
|== | 2%
|
|===== | 3%
|
|======= | 5%
|
|========= | 7%
|
|============ | 8%
|
|============== | 10%
|
|================= | 12%
|
|=================== | 13%
|
|===================== | 15%
|
|======================== | 17%
|
|========================== | 18%
|
|============================ | 20%
|
|=============================== | 22%
|
|================================= | 23%
|
|==================================== | 25%
|
|====================================== | 27%
|
|======================================== | 28%
|
|=========================================== | 30%
|
|============================================= | 32%
|
|=============================================== | 33%
|
|================================================== | 35%
|
|==================================================== | 37%
|
|====================================================== | 38%
|
|========================================================= | 40%
|
|=========================================================== | 42%
|
|============================================================== | 43%
|
|================================================================ | 45%
|
|================================================================== | 47%
|
|===================================================================== | 48%
|
|======================================================================= | 50%
|
|========================================================================= | 52%
|
|============================================================================ | 53%
|
|============================================================================== | 55%
|
|================================================================================ | 57%
|
|=================================================================================== | 58%
|
|===================================================================================== | 60%
|
|======================================================================================== | 62%
|
|========================================================================================== | 63%
|
|============================================================================================ | 65%
|
|=============================================================================================== | 67%
|
|================================================================================================= | 68%
|
|=================================================================================================== | 70%
|
|====================================================================================================== | 72%
|
|======================================================================================================== | 73%
|
|========================================================================================================== | 75%
|
|============================================================================================================= | 77%
|
|=============================================================================================================== | 78%
|
|================================================================================================================== | 80%
|
|==================================================================================================================== | 82%
|
|====================================================================================================================== | 83%
|
|========================================================================================================================= | 85%
|
|=========================================================================================================================== | 87%
|
|============================================================================================================================= | 88%
|
|================================================================================================================================ | 90%
|
|================================================================================================================================== | 92%
|
|===================================================================================================================================== | 93%
|
|======================================================================================================================================= | 95%
|
|========================================================================================================================================= | 97%
|
|============================================================================================================================================ | 98%
|
|==============================================================================================================================================| 100%Determine variable features
Setting min_variance to: -Inf
Calculating residuals of type pearson for 15224 genes
|
| | 0%
|
|== | 2%
|
|===== | 3%
|
|======= | 5%
|
|========= | 7%
|
|============ | 8%
|
|============== | 10%
|
|================= | 12%
|
|=================== | 13%
|
|===================== | 15%
|
|======================== | 17%
|
|========================== | 18%
|
|============================ | 20%
|
|=============================== | 22%
|
|================================= | 23%
|
|==================================== | 25%
|
|====================================== | 27%
|
|======================================== | 28%
|
|=========================================== | 30%
|
|============================================= | 32%
|
|=============================================== | 33%
|
|================================================== | 35%
|
|==================================================== | 37%
|
|====================================================== | 38%
|
|========================================================= | 40%
|
|=========================================================== | 42%
|
|============================================================== | 43%
|
|================================================================ | 45%
|
|================================================================== | 47%
|
|===================================================================== | 48%
|
|======================================================================= | 50%
|
|========================================================================= | 52%
|
|============================================================================ | 53%
|
|============================================================================== | 55%
|
|================================================================================ | 57%
|
|=================================================================================== | 58%
|
|===================================================================================== | 60%
|
|======================================================================================== | 62%
|
|========================================================================================== | 63%
|
|============================================================================================ | 65%
|
|=============================================================================================== | 67%
|
|================================================================================================= | 68%
|
|=================================================================================================== | 70%
|
|====================================================================================================== | 72%
|
|======================================================================================================== | 73%
|
|========================================================================================================== | 75%
|
|============================================================================================================= | 77%
|
|=============================================================================================================== | 78%
|
|================================================================================================================== | 80%
|
|==================================================================================================================== | 82%
|
|====================================================================================================================== | 83%
|
|========================================================================================================================= | 85%
|
|=========================================================================================================================== | 87%
|
|============================================================================================================================= | 88%
|
|================================================================================================================================ | 90%
|
|================================================================================================================================== | 92%
|
|===================================================================================================================================== | 93%
|
|======================================================================================================================================= | 95%
|
|========================================================================================================================================= | 97%
|
|============================================================================================================================================ | 98%
|
|==============================================================================================================================================| 100%Computing corrected UMI count matrix
|
| | 0%
|
|===== | 3%
|
|========= | 6%
|
|============== | 10%
|
|================== | 13%
|
|======================= | 16%
|
|=========================== | 19%
|
|================================ | 23%
|
|===================================== | 26%
|
|========================================= | 29%
|
|============================================== | 32%
|
|================================================== | 35%
|
|======================================================= | 39%
|
|============================================================ | 42%
|
|================================================================ | 45%
|
|===================================================================== | 48%
|
|========================================================================= | 52%
|
|============================================================================== | 55%
|
|================================================================================== | 58%
|
|======================================================================================= | 61%
|
|============================================================================================ | 65%
|
|================================================================================================ | 68%
|
|===================================================================================================== | 71%
|
|========================================================================================================= | 74%
|
|============================================================================================================== | 77%
|
|=================================================================================================================== | 81%
|
|======================================================================================================================= | 84%
|
|============================================================================================================================ | 87%
|
|================================================================================================================================ | 90%
|
|===================================================================================================================================== | 94%
|
|========================================================================================================================================= | 97%
|
|==============================================================================================================================================| 100%Place corrected count matrix in counts slot
Centering and scaling data matrix
|
| | 0%
|
|======= | 5%
|
|============== | 10%
|
|==================== | 14%
|
|=========================== | 19%
|
|================================== | 24%
|
|========================================= | 29%
|
|=============================================== | 33%
|
|====================================================== | 38%
|
|============================================================= | 43%
|
|==================================================================== | 48%
|
|========================================================================== | 52%
|
|================================================================================= | 57%
|
|======================================================================================== | 62%
|
|=============================================================================================== | 67%
|
|===================================================================================================== | 71%
|
|============================================================================================================ | 76%
|
|=================================================================================================================== | 81%
|
|========================================================================================================================== | 86%
|
|================================================================================================================================ | 90%
|
|======================================================================================================================================= | 95%
|
|==============================================================================================================================================| 100%
Set default assay to SCT
TE001 <- RunPCA(TE001, features=all.genes, verbose=F)Compute clustering solution at the gene expression level by
optimizing the silhouette score with acdc. Bigger clusters
(>100 cells) are subclustered in a second step for finer grain cell
type assignment.
Clustering (iteration 1):
if (running_clustering == TRUE){
cat("Computing clustering solution at gene expression..\n")
control <- list(maxit=1e4,
smooth=FALSE,
max.time=60*30,
temperature=1e9)
TE001 <- SAClustering(S.obj = TE001,
assay="SCT",
control=control,
type.fun = "group.mean.silhouette")
opt_param <- TE001@assays$SCT@misc$SA.history$optim.par # res, NN
} else if (running_clustering == FALSE){
cat("Using previously identified optimal solution at gene expression, without recomputing it..\n")
TE001 <- getFinal(S.obj = TE001,
assay="SCT",
res=opt_param[1],
NN=opt_param[2],
type.fun="group.mean.silhouette")
}Using previously identified optimal solution at gene expression, without recomputing it..# rename clusters such that levels start from 1 instead of 0, just for consistency
levels(TE001$seurat_clusters) <- as.character(as.numeric(levels(TE001$seurat_clusters)) + 1)Display the silhouette after the first clustering iteration.
npcs <- dim( TE001@reductions$pca@cell.embeddings )[2]
PCs<-as.data.frame(TE001$pca@cell.embeddings[,1:npcs])
euc_distance<-dist(PCs, method="euclidean")
s <- silhouette( as.integer(TE001$seurat_clusters) , euc_distance )
pdf(file.path(figures_path, "step_1_silhouette.pdf") , width = 6 , height = 8)
fviz_silhouette(s,print.summary = FALSE)
dev.off()null device
1 fviz_silhouette(s,print.summary = FALSE) 
Show clustering solution after the first iteration on a UMAP embeddings. The first UMAP visualization is the one computed at gene expression level, the second UMAP visualization is the one computed at protein activity.
npcs <- dim( TE001@reductions$pca@cell.embeddings )[2]
TE001 <- RunUMAP(TE001, dims = 1:npcs, verbose=F)
pdf(file.path(figures_path,"step_1_clustering_umap.pdf"), width = 12, height = 6) # Adjust width and height as needed
DimPlot(TE001, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"), reduction="umap", pt.size=1.5)
dev.off()null device
1 DimPlot(TE001, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"), reduction="umap", pt.size=1.5)
TE001@reductions$umap@cell.embeddings <- as.matrix( TE001@meta.data[c("UMAP_1_scanpy","UMAP_2_scanpy")] )
colnames(TE001@reductions$umap@cell.embeddings) <- c("UMAP_1", "UMAP_2")
pdf(file.path(figures_path,"step_1_clustering_umap_pa.pdf"), width = 12, height = 6)
DimPlot(TE001, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"), reduction="umap", pt.size=1.5)dev.off()quartz_off_screen
2 
DimPlot(TE001, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"), reduction="umap", pt.size=1.5)
Clustering (iteration 2):
# Split by cluster and subcluster
TE001_list <- SplitObject(object=TE001, split.by="seurat_clusters")
TE001_clusters <- TE001$seurat_clusters %>% unique()
cat("Subclustering only clusters with at least 100 cells..\n")Subclustering only clusters with at least 100 cells..n_cells_per_cluster <- sapply(TE001_list, function(x){dim(x)[2]})
TE001_clusters_to_subcluster <- TE001_clusters[n_cells_per_cluster >=100]
if (running_clustering == TRUE){
optim_par_list <- matrix(NA, nrow=length(TE001_clusters_to_subcluster), ncol=2)
colnames(optim_par_list) <- c("res", "NN")
rownames(optim_par_list) <- as.character(TE001_clusters_to_subcluster)
cat("Subclustering on a cluster by cluster basis..\n")
for (k in TE001_clusters_to_subcluster){
TE001_list[[k]] <- SAClustering(S.obj = TE001_list[[k]],
assay="SCT",
control=control,
type.fun = "group.mean.silhouette")
# rename clusters such that levels start from 1 instead of 0
levels(TE001_list[[k]]@meta.data$seurat_clusters) <- as.character(as.numeric(levels(TE001_list[[k]]@meta.data$seurat_clusters)) + 1)
# store name for branching
TE001_list[[k]]@meta.data$seurat_clusters <- paste0(k,"_",TE001_list[[k]]@meta.data$seurat_clusters)
optim_par_list[k,] <- TE001_list[[k]]@assays$SCT@misc$SA.history$optim.par # res, NN
}
} else if (running_clustering == FALSE){
cat("Subclustering on a cluster by cluster basis by using the previously optimized clustering parameters..\n")
for (k in TE001_clusters_to_subcluster){
opt_res <- optim_par_list[k,"res"]
opt_NN <- optim_par_list[k,"NN"]
TE001_list[[k]] <- getFinal(S.obj = TE001_list[[k]],
assay="SCT",
res=opt_res,
NN=opt_NN,
type.fun = "group.mean.silhouette")
levels(TE001_list[[k]]@meta.data$seurat_clusters) <- as.character(as.numeric(levels(TE001_list[[k]]@meta.data$seurat_clusters)) + 1)
TE001_list[[k]]@meta.data$seurat_clusters <- paste0(k,"_",TE001_list[[k]]@meta.data$seurat_clusters)
}
}Subclustering on a cluster by cluster basis by using the previously optimized clustering parameters..
TE001_list[[k]]@meta.data$seurat_clusters <- as.factor(TE001_list[[k]]@meta.data$seurat_clusters)
# remerge Seurat objects into one Seurat object
TE001_subclustering <- merge(x=TE001_list[[1]],
y=TE001_list[2:length(TE001_list)])Display the silhouette after the second clustering iteration.
# Re-run PCA to visualize it
TE001_subclustering <- RunPCA(TE001_subclustering, features=all.genes, verbose=F)
npcs <- dim( TE001_subclustering@reductions$pca@cell.embeddings )[2]
PCs<-as.data.frame(TE001_subclustering$pca@cell.embeddings[,1:npcs])
euc_distance<-dist(PCs, method="euclidean")
TE001_subclustering@meta.data$seurat_clusters <- as.factor(TE001_subclustering@meta.data$seurat_clusters)
s <- silhouette( as.integer(TE001_subclustering$seurat_clusters) , euc_distance )
pdf(file.path(figures_path, "step_2_silhouette.pdf") , width = 6 , height = 8)
fviz_silhouette(s,print.summary = FALSE)
dev.off()null device
1
fviz_silhouette(s,print.summary = FALSE) 
Show clustering solution after the first iteration on a UMAP embeddings. The first UMAP visualization is the one computed at gene expression level, the second UMAP visualization is the one computed at protein activity.
TE001_subclustering <- RunUMAP(TE001_subclustering, dims = 1:npcs, verbose=F)
pdf(file.path(figures_path,"step_2_clustering_umap.pdf"), width = 12, height = 6)
DimPlot(TE001_subclustering, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"),
reduction="umap", pt.size=1.5 )
dev.off()null device
1
DimPlot(TE001_subclustering, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"),
reduction="umap", pt.size=1.5 )
TE001_subclustering@reductions$umap@cell.embeddings <- as.matrix( TE001_subclustering@meta.data[c("UMAP_1_scanpy","UMAP_2_scanpy")] )
colnames(TE001_subclustering@reductions$umap@cell.embeddings) <- c("UMAP_1", "UMAP_2")
pdf(file.path(figures_path,"step_2_clustering_umap_pa.pdf"), width = 12, height = 6)
DimPlot(TE001_subclustering, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"),
reduction="umap", pt.size=1.5 )dev.off()quartz_off_screen
2 
DimPlot(TE001_subclustering, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"),
reduction="umap", pt.size=1.5 )
Display cytotrace score on a UMAP.
pdf(file.path(figures_path,"step_2_clustering_umap_pa_cytotrace.pdf"), width = 6, height = 6)
FeaturePlot(TE001_subclustering, features = "cytotrace_te001", pt.size=1.5) & scale_color_viridis_c()Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.dev.off()null device
1 FeaturePlot(TE001_subclustering, features = "cytotrace_te001", pt.size=1.5) & scale_color_viridis_c()Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Perform differential gene expression analysis on the identified clusters after the solution optimized in 2 steps.
# Perform differential gene expression analysis
cat("Perform differential gene expression analysis on the identified clusters..\n")Perform differential gene expression analysis on the identified clusters..Idents(TE001_subclustering) <- "seurat_clusters"
# Use RNA slot for calculation of DE genes
TE001_subclustering <- NormalizeData(TE001_subclustering, assay="RNA", normalization.method = "LogNormalize")Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
# FindAllMarkers
TE001_subclustering_markers <- FindAllMarkers(TE001_subclustering,
assay = "RNA",
slot="data",
test.use = "wilcox",
only.pos = TRUE,
min.pct = 0.25)Calculating cluster 1_1
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~05s
|++ | 3 % ~05s
|+++ | 4 % ~05s
|+++ | 6 % ~05s
|++++ | 7 % ~05s
|++++ | 8 % ~05s
|+++++ | 9 % ~05s
|+++++ | 10% ~05s
|++++++ | 11% ~05s
|+++++++ | 12% ~05s
|+++++++ | 13% ~05s
|++++++++ | 14% ~05s
|++++++++ | 16% ~04s
|+++++++++ | 17% ~04s
|+++++++++ | 18% ~04s
|++++++++++ | 19% ~04s
|++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|++++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|+++++++++++++ | 24% ~04s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|+++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|+++++++++++++++++ | 32% ~04s
|+++++++++++++++++ | 33% ~04s
|++++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|+++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s Calculating cluster 1_2
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 4 % ~01s
|+++ | 6 % ~01s
|++++ | 7 % ~01s
|++++ | 8 % ~01s
|+++++ | 9 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 14% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 1_3
| | 0 % ~calculating
|+ | 1 % ~11s
|++ | 2 % ~11s
|++ | 3 % ~10s
|+++ | 4 % ~10s
|+++ | 5 % ~10s
|++++ | 6 % ~10s
|++++ | 7 % ~10s
|+++++ | 8 % ~10s
|+++++ | 9 % ~10s
|++++++ | 10% ~10s
|++++++ | 11% ~09s
|+++++++ | 12% ~09s
|+++++++ | 14% ~09s
|++++++++ | 15% ~09s
|++++++++ | 16% ~09s
|+++++++++ | 17% ~09s
|+++++++++ | 18% ~09s
|++++++++++ | 19% ~09s
|++++++++++ | 20% ~09s
|+++++++++++ | 21% ~08s
|+++++++++++ | 22% ~08s
|++++++++++++ | 23% ~08s
|++++++++++++ | 24% ~08s
|+++++++++++++ | 25% ~08s
|++++++++++++++ | 26% ~08s
|++++++++++++++ | 27% ~08s
|+++++++++++++++ | 28% ~08s
|+++++++++++++++ | 29% ~08s
|++++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|+++++++++++++++++ | 32% ~07s
|+++++++++++++++++ | 33% ~07s
|++++++++++++++++++ | 34% ~07s
|++++++++++++++++++ | 35% ~07s
|+++++++++++++++++++ | 36% ~07s
|+++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 39% ~07s
|++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|+++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~06s
|++++++++++++++++++++++ | 44% ~06s
|+++++++++++++++++++++++ | 45% ~06s
|+++++++++++++++++++++++ | 46% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~05s
|+++++++++++++++++++++++++ | 50% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|+++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|++++++++++++++++++++++++++++ | 54% ~05s
|++++++++++++++++++++++++++++ | 55% ~05s
|+++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|++++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|+++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|++++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s Calculating cluster 2_1
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~05s
|++ | 3 % ~05s
|+++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 7 % ~05s
|+++++ | 8 % ~05s
|+++++ | 9 % ~04s
|++++++ | 10% ~04s
|++++++ | 11% ~04s
|+++++++ | 12% ~04s
|+++++++ | 13% ~04s
|++++++++ | 14% ~04s
|++++++++ | 15% ~04s
|+++++++++ | 16% ~04s
|+++++++++ | 17% ~04s
|++++++++++ | 18% ~04s
|++++++++++ | 19% ~04s
|+++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|++++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|+++++++++++++ | 24% ~04s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|+++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s Calculating cluster 2_2
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~08s
|++ | 3 % ~07s
|+++ | 4 % ~07s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 8 % ~07s
|+++++ | 9 % ~07s
|+++++ | 10% ~07s
|++++++ | 11% ~07s
|++++++ | 12% ~07s
|+++++++ | 13% ~07s
|+++++++ | 14% ~07s
|++++++++ | 15% ~06s
|+++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|+++++++++++ | 20% ~06s
|+++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|+++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 37% ~05s
|+++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|+++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~03s
|++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|++++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|+++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s Calculating cluster 3_1
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 8 % ~03s
|+++++ | 9 % ~03s
|+++++ | 10% ~03s
|++++++ | 11% ~03s
|++++++ | 12% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 3_2
| | 0 % ~calculating
|+ | 1 % ~18s
|++ | 2 % ~17s
|++ | 3 % ~17s
|+++ | 4 % ~17s
|+++ | 5 % ~17s
|++++ | 6 % ~17s
|++++ | 7 % ~17s
|+++++ | 8 % ~16s
|+++++ | 9 % ~16s
|++++++ | 10% ~16s
|++++++ | 11% ~16s
|+++++++ | 12% ~16s
|+++++++ | 13% ~15s
|++++++++ | 14% ~15s
|++++++++ | 15% ~15s
|+++++++++ | 16% ~15s
|+++++++++ | 17% ~15s
|++++++++++ | 18% ~15s
|++++++++++ | 19% ~14s
|+++++++++++ | 20% ~14s
|+++++++++++ | 21% ~14s
|++++++++++++ | 22% ~14s
|++++++++++++ | 23% ~14s
|+++++++++++++ | 24% ~13s
|+++++++++++++ | 26% ~13s
|++++++++++++++ | 27% ~13s
|++++++++++++++ | 28% ~13s
|+++++++++++++++ | 29% ~13s
|+++++++++++++++ | 30% ~13s
|++++++++++++++++ | 31% ~12s
|++++++++++++++++ | 32% ~12s
|+++++++++++++++++ | 33% ~12s
|+++++++++++++++++ | 34% ~12s
|++++++++++++++++++ | 35% ~12s
|++++++++++++++++++ | 36% ~12s
|+++++++++++++++++++ | 37% ~11s
|+++++++++++++++++++ | 38% ~11s
|++++++++++++++++++++ | 39% ~11s
|++++++++++++++++++++ | 40% ~11s
|+++++++++++++++++++++ | 41% ~11s
|+++++++++++++++++++++ | 42% ~10s
|++++++++++++++++++++++ | 43% ~10s
|++++++++++++++++++++++ | 44% ~10s
|+++++++++++++++++++++++ | 45% ~10s
|+++++++++++++++++++++++ | 46% ~10s
|++++++++++++++++++++++++ | 47% ~10s
|++++++++++++++++++++++++ | 48% ~09s
|+++++++++++++++++++++++++ | 49% ~09s
|+++++++++++++++++++++++++ | 50% ~09s
|++++++++++++++++++++++++++ | 51% ~09s
|+++++++++++++++++++++++++++ | 52% ~09s
|+++++++++++++++++++++++++++ | 53% ~08s
|++++++++++++++++++++++++++++ | 54% ~08s
|++++++++++++++++++++++++++++ | 55% ~08s
|+++++++++++++++++++++++++++++ | 56% ~08s
|+++++++++++++++++++++++++++++ | 57% ~08s
|++++++++++++++++++++++++++++++ | 58% ~08s
|++++++++++++++++++++++++++++++ | 59% ~07s
|+++++++++++++++++++++++++++++++ | 60% ~07s
|+++++++++++++++++++++++++++++++ | 61% ~07s
|++++++++++++++++++++++++++++++++ | 62% ~07s
|++++++++++++++++++++++++++++++++ | 63% ~07s
|+++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|++++++++++++++++++++++++++++++++++ | 66% ~06s
|++++++++++++++++++++++++++++++++++ | 67% ~06s
|+++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 69% ~06s
|++++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|+++++++++++++++++++++++++++++++++++++ | 72% ~05s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|++++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=18s Calculating cluster 3_3
| | 0 % ~calculating
|+ | 1 % ~00s
|++ | 3 % ~00s
|+++ | 4 % ~00s
|+++ | 6 % ~00s
|++++ | 7 % ~00s
|+++++ | 9 % ~00s
|++++++ | 10% ~00s
|++++++ | 12% ~00s
|+++++++ | 13% ~00s
|++++++++ | 14% ~00s
|++++++++ | 16% ~00s
|+++++++++ | 17% ~00s
|++++++++++ | 19% ~00s
|+++++++++++ | 20% ~00s
|+++++++++++ | 22% ~00s
|++++++++++++ | 23% ~00s
|+++++++++++++ | 25% ~00s
|++++++++++++++ | 26% ~00s
|++++++++++++++ | 28% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++ | 30% ~00s
|++++++++++++++++ | 32% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++ | 35% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++ | 39% ~00s
|+++++++++++++++++++++ | 41% ~00s
|++++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~10s
|+ | 2 % ~10s
|++ | 3 % ~09s
|++ | 4 % ~09s
|+++ | 5 % ~09s
|+++ | 6 % ~09s
|++++ | 7 % ~09s
|++++ | 8 % ~09s
|+++++ | 9 % ~09s
|+++++ | 10% ~09s
|++++++ | 11% ~09s
|++++++ | 12% ~08s
|+++++++ | 13% ~08s
|+++++++ | 14% ~08s
|++++++++ | 15% ~08s
|++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|+++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|++++++++++ | 20% ~08s
|+++++++++++ | 21% ~08s
|+++++++++++ | 22% ~08s
|++++++++++++ | 23% ~07s
|++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|+++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|+++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|++++++++++++++++ | 32% ~07s
|+++++++++++++++++ | 33% ~07s
|+++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~06s
|++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 37% ~06s
|+++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|+++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~06s
|++++++++++++++++++++++ | 44% ~06s
|+++++++++++++++++++++++ | 45% ~05s
|+++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|+++++++++++++++++++++++++ | 50% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|+++++++++++++++++++++++++++ | 54% ~05s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|+++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|+++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s
top10 <- TE001_subclustering_markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)Generate a heatmap showing the top 10 differentially expressed genes on a per-cluster basis
genes <- top10$gene
mat_to_show <- as.matrix(TE001_subclustering@assays$SCT@scale.data[genes,])
Clusters <- TE001_subclustering$seurat_clusters
colors <- rainbow(nlevels(Clusters))
names(colors) <- levels(Clusters)
# prepare df_annot.df
per.cell.sil.width <- s[,"sil_width"]
names(per.cell.sil.width) <- colnames(TE001_subclustering)
df_annot.df <- TE001_subclustering@meta.data %>%
dplyr::select(cluster_id=seurat_clusters, cytotrace_score = cytotrace_te001) %>%
tibble::rownames_to_column(.,var="cell_id")
df_annot.df$cluster_id <- factor(as.numeric(df_annot.df$cluster_id))
x <- as.data.frame(per.cell.sil.width) %>%
tibble::rownames_to_column(.,var="sample_id")
df_annot.df <- left_join(df_annot.df,x,by=c("cell_id"="sample_id"))
df_annot.df <- merge(df_annot.df, TE001_subclustering@meta.data[, c("cell_id", "seurat_clusters")], by = "cell_id", all.x = TRUE) # rename clusters as a tree-like structure
df_annot.df <- df_annot.df[,-2] %>% dplyr::rename(cluster_id=seurat_clusters)
sample_ordering_to_print <- rev( order( df_annot.df$per.cell.sil.width ) )
df_annot.df <- df_annot.df[sample_ordering_to_print,]
mat_to_show <- mat_to_show[,match(df_annot.df$cell_id,colnames(mat_to_show))]
stopifnot(identical( df_annot.df$cell_id, colnames(mat_to_show) ))
# the silhouette score
sil.fill <- rep(NA, nrow(df_annot.df))
unique_clusters <- unique(df_annot.df$cluster_id)
n_colors <- length(colors)
color_index <- 1
for (i in seq_along(unique_clusters)) {
cluster <- unique_clusters[i]
sil.fill[df_annot.df$cluster_id == cluster] <- colors[names(colors) == cluster]
color_index <- i + 1 # Use modulo to cycle through colors
}
df_annot_cols_cluster_score <- HeatmapAnnotation(
silhouette_score = anno_barplot(
df_annot.df$per.cell.sil.width,
bar_width = 1,
gp = gpar(col = NA,
fontsize = 6 ,
cex = 0.8 ,
fill = sil.fill
),
border = FALSE,
height = unit(1.25, "cm") ,
width = unit(0.5, "cm")
)
)
# heatmap annotation
df_annot.df <- df_annot.df %>% dplyr::select(cluster_id)
cluster_colors <- colors[ !is.na(names(colors)) ]
df_annot_cols = HeatmapAnnotation( df = df_annot.df,
col = list(cluster_id=cluster_colors) ,
show_legend = TRUE , na_col = "gray95",
annotation_name_gp = gpar(fontsize=0.1)
)
top10_hm <- Heatmap( mat_to_show,
col = circlize::colorRamp2(range(mat_to_show_default)/2, c("darkslateblue", "yellow")),
use_raster = FALSE ,
column_split = df_annot.df$cluster_id,
heatmap_legend_param = list(color_bar = "continuous") ,
cluster_columns = FALSE ,
cluster_rows = FALSE ,
show_column_names = FALSE ,
name = "Gene Expression (log1p)" ,
row_names_gp = gpar(fontsize = 10) , column_names_gp = gpar(fontsize = 8),
row_title_side = "left" , row_title_gp = gpar(fontsize = 10, fontface = "bold")
)
ht_list = df_annot_cols_cluster_score %v% df_annot_cols %v% top10_hm
pdf(file.path(figures_path,"top_10_heatmap.pdf"), width = 10, height = 12)
p <- draw(ht_list, column_km = 1 ,
heatmap_legend_side = "right",
annotation_legend_side = "bottom" )
dev.off() null device
1 p
Display a dotplot with the top 5 genes per cluster (using the
data slot).
# DotPlot
top5 <- TE001_subclustering_markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_log2FC)
genes_dp <- top5$gene %>% unique()
# intialize lists containing dataframes
Avgs <- vector( mode="list", length=length(levels(TE001_subclustering$seurat_clusters)) )
names(Avgs) <- levels(TE001_subclustering$seurat_clusters)
Pct <- vector( mode="list", length=length(levels(TE001_subclustering$seurat_clusters)) )
names(Pct) <- levels(TE001_subclustering$seurat_clusters)
for (cluster in levels(TE001_subclustering$seurat_clusters)){
cells_in_subcluster <- TE001_subclustering$seurat_clusters == cluster
# Avg
Avgs[[cluster]] <- TE001_subclustering[["RNA"]]@data[genes_dp, cells_in_subcluster] %>%
rowMeans() %>%
as.data.frame() %>%
rownames_to_column() %>%
dplyr::rename(.,"Gene"=rowname,"mean"=.) %>%
dplyr::mutate(cluster=cluster)
# Pct
Pct[[cluster]] <- TE001_subclustering[["RNA"]]@data[genes_dp, cells_in_subcluster] %>%
apply(.,1, function(x){ sum(x>0)/length(x)*100 }) %>%
as.data.frame() %>%
rownames_to_column() %>%
dplyr::rename(.,"Gene"=rowname,"Pct"=.) %>%
dplyr::mutate(cluster=cluster)
}
# Mean
df_mean.GEx <- do.call(rbind, Avgs)
rownames(df_mean.GEx) <- NULL
# Pct
df_pct.GEx <- do.call(rbind, Pct)
rownames(df_pct.GEx) <- NULL
# Summary
summary.GEx <- cbind(df_mean.GEx[,1:2],df_pct.GEx[,2:3])
summary.GEx$Gene <- factor(summary.GEx$Gene, levels=genes_dp, ordered = TRUE)
# Printing dotplot for genes
cat("Printing dotplots..\n")Printing dotplots..GEx.DGE_DotPlot <- ggplot(summary.GEx, aes(x=Gene,y=cluster)) +
geom_point(aes(size = Pct, fill= mean), color="black", shape=21) +
scale_size("% detected", range = c(0,15), breaks=c(0,25,50,75,100), limits=c(0,100)) +
scale_fill_gradient2(low="steelblue1", high = "red", mid="white",midpoint=0,
limits=range(summary.GEx$mean),
guide = guide_colorbar(ticks.colour = "black",
frame.colour = "black"),
name = "Average Expression\nlog1p(counts)") +
ylab("Cluster") + xlab("") +
theme_bw() +
theme(axis.text.x = element_text(size=10, angle=45, hjust=1, color="black"),
axis.text.y = element_text(size=12, color="black"),
axis.title = element_text(size=14) ,
panel.grid.major = element_blank(), panel.grid.minor = element_blank()
)
pdf(file.path(figures_path, "GEx-DGE-dotplot.pdf") , width = 22 , height = 9)
print(GEx.DGE_DotPlot)
dev.off()null device
1
print(GEx.DGE_DotPlot)
Save Seurat Object with clustering solution as an .rds
file to the DataFolder location.
saveRDS(object=TE001_subclustering, file=file.path(savingsFolder, "TE001_iter_subclustering_gene_expr.rds"))sessionInfo()R version 4.3.0 (2023-04-21)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 14.0
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] GenSA_1.1.8 SeuratDisk_0.0.0.9021 pbmc3k.SeuratData_3.1.4 SeuratData_0.2.2 gridExtra_2.3 circlize_0.4.15
[7] RColorBrewer_1.1-3 ComplexHeatmap_2.16.0 factoextra_1.0.7 cluster_2.1.6 acdc_0.1.0 tibble_3.2.1
[13] SeuratObject_4.1.3 Seurat_4.3.0.1 matrixStats_1.0.0 metap_1.8 openxlsx_4.2.5.2 org.Hs.eg.db_3.17.0
[19] AnnotationDbi_1.62.1 IRanges_2.34.1 S4Vectors_0.38.1 Biobase_2.60.0 BiocGenerics_0.46.0 RobustRankAggreg_1.2.1
[25] MOMA_1.12.0 ggplot2_3.4.2 dplyr_1.1.2
loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.2 goftest_1.2-3 Biostrings_2.68.1 TH.data_1.1-2 vctrs_0.6.3
[6] spatstat.random_3.1-5 digest_0.6.31 png_0.1-8 shape_1.4.6 ggrepel_0.9.3
[11] deldir_1.0-9 parallelly_1.36.0 MASS_7.3-60 reshape2_1.4.4 httpuv_1.6.11
[16] foreach_1.5.2 qvalue_2.32.0 withr_2.5.0 ggrastr_1.0.2 xfun_0.39
[21] ggpubr_0.6.0 ellipsis_0.3.2 survival_3.5-5 memoise_2.0.1 ggbeeswarm_0.7.2
[26] zoo_1.8-12 GlobalOptions_0.1.2 pbapply_1.7-2 R.oo_1.25.0 DEoptimR_1.0-14
[31] KEGGREST_1.40.0 promises_1.2.0.1 httr_1.4.6 rstatix_0.7.2 globals_0.16.2
[36] fitdistrplus_1.1-11 rstudioapi_0.14 miniUI_0.1.1.1 generics_0.1.3 zlibbioc_1.46.0
[41] polyclip_1.10-4 TFisher_0.2.0 GenomeInfoDbData_1.2.10 xtable_1.8-4 stringr_1.5.0
[46] doParallel_1.0.17 evaluate_0.21 S4Arrays_1.0.4 MKmisc_1.9 hms_1.1.3
[51] GenomicRanges_1.52.0 irlba_2.3.5.1 colorspace_2.1-0 hdf5r_1.3.8 ROCR_1.0-11
[56] reticulate_1.30 readxl_1.4.2 spatstat.data_3.0-1 magrittr_2.0.3 lmtest_0.9-40
[61] readr_2.1.4 later_1.3.1 viridis_0.6.3 lattice_0.21-8 spatstat.geom_3.2-1
[66] future.apply_1.11.0 robustbase_0.99-0 scattermore_1.2 cowplot_1.1.1 RcppAnnoy_0.0.21
[71] pillar_1.9.0 nlme_3.1-162 iterators_1.0.14 compiler_4.3.0 stringi_1.7.12
[76] tensor_1.5 SummarizedExperiment_1.30.2 plyr_1.8.8 crayon_1.5.2 abind_1.4-5
[81] sn_2.1.1 sp_2.0-0 bit_4.0.5 mathjaxr_1.6-0 sandwich_3.0-2
[86] codetools_0.2-19 multcomp_1.4-25 bslib_0.5.0 GetoptLong_1.0.5 plotly_4.10.2
[91] multtest_2.56.0 mime_0.12 MultiAssayExperiment_1.26.0 splines_4.3.0 Rcpp_1.0.10
[96] cellranger_1.1.0 knitr_1.43 blob_1.2.4 utf8_1.2.3 clue_0.3-64
[101] listenv_0.9.0 Rdpack_2.4 ggsignif_0.6.4 Matrix_1.5-4.1 tzdb_0.4.0
[106] pkgconfig_2.0.3 tools_4.3.0 cachem_1.0.8 rbibutils_2.2.13 RSQLite_2.3.1
[111] viridisLite_0.4.2 DBI_1.1.3 numDeriv_2016.8-1.1 fastmap_1.1.1 rmarkdown_2.23
[116] scales_1.2.1 ica_1.0-3 broom_1.0.5 sass_0.4.6 patchwork_1.1.2
[121] carData_3.0-5 RANN_2.6.1 farver_2.1.1 yaml_2.3.7 MatrixGenerics_1.12.2
[126] cli_3.6.1 purrr_1.0.1 leiden_0.4.3 lifecycle_1.0.3 rsconnect_1.2.0
[131] uwot_0.1.16 mvtnorm_1.2-2 backports_1.4.1 gtable_0.3.3 rjson_0.2.21
[136] ggridges_0.5.4 progressr_0.13.0 parallel_4.3.0 limma_3.56.2 jsonlite_1.8.7
[141] bitops_1.0-7 bit64_4.0.5 qqconf_1.3.2 Rtsne_0.16 spatstat.utils_3.0-3
[146] zip_2.3.0 mutoss_0.1-13 jquerylib_0.1.4 R.utils_2.12.2 lazyeval_0.2.2
[151] shiny_1.7.4 htmltools_0.5.5 sctransform_0.3.5 rappdirs_0.3.3 glue_1.6.2
[156] XVector_0.40.0 RCurl_1.98-1.12 mnormt_2.1.1 igraph_1.5.0 R6_2.5.1
[161] tidyr_1.3.0 labeling_0.4.2 GenomeInfoDb_1.36.1 DelayedArray_0.26.3 tidyselect_1.2.0
[166] vipor_0.4.5 plotrix_3.8-2 car_3.1-2 future_1.33.0 munsell_0.5.0
[171] KernSmooth_2.23-21 data.table_1.14.8 htmlwidgets_1.6.2 rlang_1.1.1 spatstat.sparse_3.0-2
[176] spatstat.explore_3.2-1 fansi_1.0.4 beeswarm_0.4.0 ---
title: "gene-expr-clustering-Seurat.R"
output:
  pdf_document: default
  html_notebook: default
---

This [R Markdown](http://rmarkdown.rstudio.com) Notebook reproduces the analysis of TE001 at the gene expression level. The first section of the Tutorial makes use of the default parameters of the Seurat pipeline. The second part of the tutorial finds a close-to-optimal clustering solution obtained with the Louvain algorithm, by returning the solution with the highest silhouette by optimizing over two parameters, the number of nearest neighbors (`NN`) and the resolution (`res`).

### 1. Setup directories and user-defined parameters

Clean the environment.

```{r}
rm(list = ls()) 
```

Set paths to the folders containing data, metadata, output figures and saving directory (to save)

```{r}
DataFolder <- "/Volumes/ac_lab_scratch/lz2841/ics-rebuttal/TE001/filtered_feature_bc_matrix/"
metadata_path <- "/Volumes/ac_lab_scratch/lz2841/ics-rebuttal/TE001-h5ad/"
savingsFolder <- DataFolder
figures_path = "/Volumes/ac_lab_scratch/lz2841/ics-rebuttal/figures/TE001_gExpr_clustering/"
```

Decide whether to optimize the clustering solution (later on, in this markdown document) or whether to use previously optimized clustering parameters.

```{r}
running_clustering <- FALSE # do not re-run clustering
cat("'runnig_clustering' is set to: ", running_clustering,"\n")
```

Define previously determined clustering parameters (not used if `running_clustering` is set to `FALSE`). These parameters were obtained with a iterative subclustering routine, as the one shown in the following sections using the `acdc` package available at: <https://github.com/califano-lab/acdc>. While the reproducibility of the obtain solution was tested, future updates of the package or different laptop configuration might result in different solutions. If this is the case, we recommend: (1) loading the Seurat object generated with this tutorial or (2) re-running the clustering optimization from the scratch, by setting `running_clustering = TRUE`. For enhanced performance, `acdc` comes also with a Grid Search-based optimization that allows for multi-thread, thus speeding up the computation.

```{r}
# optimal clustering parameters - previously determined
# step 1
opt_param <- c(0.03013859, 22) # res NN 

# step 2 (subclustering)
optim_par_list <- matrix(c(0.05655232, 7, 0.0876204, 8, 0.06659604, 7),byrow=TRUE,nrow=3,ncol=2)
colnames(optim_par_list) <- c("res", "NN")
rownames(optim_par_list) <- c("1", "3", "2")
```

Load libraries.

```{r}
library(dplyr)
library(matrixStats)
library(Seurat)
library(tibble)
library(acdc)
library(cluster)
library(ggplot2)
library(factoextra)
library(ComplexHeatmap)
library(RColorBrewer)
library(circlize)
library(gridExtra)
```

### 2. Load data, perform quality control and cell filtering

Define thresholds for cell filtering.

```{r}
min_cells <- 5
min_feats <- 1000
  
min_nFeature_RNA <- 1500
max_nFeature_RNA <- 20000
max_nCount_RNA <- 100000
mt_percent_threshold <- 10
```

Load UMI counts and create Seurat Object.

```{r}
# counts
TE001_data <- Read10X(file.path(data.dir=file.path(DataFolder))) # load counts

# generate Seurat object  
TE001 <- CreateSeuratObject(counts=TE001_data,
                            project="TE001",
                            assay="RNA",
                            min.cells = min_cells,
                            min.features = min_feats)
  
TE001@meta.data$cell_id <- rownames(TE001@meta.data) 
TE001@meta.data <- TE001@meta.data %>% dplyr::select(.,cell_id, everything())
```

Visualize QC as violin plots before filtering.

```{r}
TE001[["percent.mt"]] <- PercentageFeatureSet(TE001, pattern="^mt")
  
VlnPlot(TE001, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size=0)
```

Filter out low quality cells and visualize QC plots after filtering.

```{r,  fig.width=12, fig.height=6}
# Filter out cells
TE001 <- subset(TE001, subset = nFeature_RNA > min_nFeature_RNA & nFeature_RNA < max_nFeature_RNA & nCount_RNA < max_nCount_RNA & percent.mt < mt_percent_threshold)
  
# Visualize QC metrics as a violin plot AFTER filtering
VlnPlot(TE001, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size=0)
  
# FeatureScatter is typically used to visualize feature-feature relationships
plot1 <- FeatureScatter(TE001, feature1 = "nCount_RNA", feature2 = "percent.mt", col="darkgray")
plot2 <- FeatureScatter(TE001, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", col="darkgray")

grid.arrange(plot1, plot2, ncol = 2)
```

Load additional metadata and include in Seurat object.

```{r}
# load additional metadata for the cells
metadata_csv <- file.path(metadata_path, "TE001-metadata-umap-and-clusters-for-paper.csv")
metadata <- read.csv(metadata_csv)

# join metadata to Seurat object
TE001@meta.data <- TE001@meta.data %>% dplyr::left_join(., metadata, 
                                                        by = c("cell_id" = "cell_id"))
rownames(TE001@meta.data) <- TE001@meta.data$cell_id
```

### 3. Analysis of TE001 with standard Seurat workflow and clustering with default parameters 

Log-Normalize data, find and display highly variable genes and scale data.

```{r}
# Normalize Data
TE001 <- NormalizeData(TE001, normalization.method = "LogNormalize", scale.factor = 10000)
  
# Finding Variable features
TE001 <- FindVariableFeatures(TE001, selection.method = "vst", nfeatures = 2000)
top10_hvg <- head(VariableFeatures(TE001), 10)
plot1 <- VariableFeaturePlot(TE001)
plot2 <- LabelPoints(plot = plot1, points = top10_hvg, repel = TRUE, xnudge=0, ynudge=0)
plot2
dev.off()
  
# Scaling the data
all.genes <- rownames(TE001)
TE001 <- ScaleData(TE001, features = all.genes)
```

Cluster cells with Louvain algorithm. 0.5 is used as the resolution value, as in <https://satijalab.org/seurat/articles/pbmc3k_tutorial>.

```{r}
# Perform linear dimensionality reduction
TE001 <- RunPCA(TE001, verbose=F)

# Cluster the cells with default parameters
TE001 <- FindNeighbors(TE001, k.param = 20)
TE001 <- FindClusters(TE001, resolution = 0.5) #
  
levels(TE001@meta.data$seurat_clusters) <- as.character(as.numeric(levels(TE001@meta.data$seurat_clusters)) + 1)
```

Evaluate the quality of clustering solution by visualizing the Silhouette Score.

```{r}
# evaluate clustering solution
PCs<-as.data.frame(TE001$pca@cell.embeddings[,1:10])
euc_distance<-dist(PCs, method="euclidean")
  
s <- silhouette( as.integer(TE001$seurat_clusters) , euc_distance )
pdf(file.path(figures_path, "silhouette_default.pdf") , width = 6 , height = 8)
fviz_silhouette(s,print.summary = FALSE)	
dev.off()

fviz_silhouette(s,print.summary = FALSE)	

```

Visualize clusters at gene expression on a UMAP embedding.

```{r, fig.width=6, fig.height=6}
TE001 <- RunUMAP(TE001, dims = 1:10)
  
pdf(file.path(figures_path, "umap_clusters_default.pdf") ,width = 12, height = 6)
DimPlot(TE001, reduction = "umap", pt.size=1.5, group.by = c("seurat_clusters","iter_cluster_id_with_paneth"))
  dev.off()
  
pdf(file.path(figures_path, "umap_default_cytotrace.pdf") , width = 6 , height = 6)
FeaturePlot(TE001, features = "cytotrace_te001", pt.size=1.5) & scale_color_viridis_c()
dev.off()

# Show figures in cell
DimPlot(TE001, reduction = "umap", pt.size=1.5, group.by = "seurat_clusters")

DimPlot(TE001, reduction = "umap", pt.size=1.5, group.by = "iter_cluster_id_with_paneth")

FeaturePlot(TE001, features = "cytotrace_te001", pt.size=1.5) & scale_color_viridis_c()

```

Perform differential gene expression analysis on the identified clusters with default Seurat pipeline

```{r}
Idents(TE001) <- "seurat_clusters"
    
# FindAllMarkers with default parameters
TE001_markers <- FindAllMarkers(TE001, 
                        assay = "RNA",
                        slot="data",
                        test.use = "wilcox", 
                        only.pos = TRUE)
    
top10_markers <- TE001_markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
genes_default <- top10_markers$gene
```

Generate a heatmap showing the top 10 differentially expressed genes on a per-cluster basis

```{r, fig.width=18, fig.height=24}
# Visualize clusters and top DE genes on a heatmap
mat_to_show_default <- as.matrix(TE001@assays$RNA@data[genes_default,])
Clusters <- TE001$seurat_clusters
colors <- rainbow(nlevels(Clusters)) 
names(colors) <- levels(Clusters)
  
# prepare df_annot.df 
per.cell.sil.width <- s[,"sil_width"]
names(per.cell.sil.width) <- colnames(TE001)
  
df_annot.df <- TE001@meta.data %>%
  dplyr::select(cluster_id=seurat_clusters, cytotrace_score = cytotrace_te001) %>%
  tibble::rownames_to_column(.,var="cell_id")
df_annot.df$cluster_id <- factor(as.numeric(df_annot.df$cluster_id))
   
x <- as.data.frame(per.cell.sil.width) %>%
  tibble::rownames_to_column(.,var="sample_id") 
  
df_annot.df <- left_join(df_annot.df,x,by=c("cell_id"="sample_id"))
  
sample_ordering_to_print <- rev( order( df_annot.df$per.cell.sil.width ) )
df_annot.df <- df_annot.df[sample_ordering_to_print,]
  
mat_to_show_default <- mat_to_show_default[,match(df_annot.df$cell_id,colnames(mat_to_show_default))]
  
stopifnot(identical( df_annot.df$cell_id, colnames(mat_to_show_default) ))
  
# the silhouette score
sil.fill <- rep(NA, nrow(df_annot.df))
unique_clusters <- unique(df_annot.df$cluster_id)
n_colors <- length(colors)
  
color_index <- 1
for (i in seq_along(unique_clusters)) {
  cluster <- unique_clusters[i]
  sil.fill[df_annot.df$cluster_id == cluster] <- colors[names(colors) == cluster]
  color_index <- i + 1  # Use modulo to cycle through colors
}
  
df_annot_cols_cluster_score <- HeatmapAnnotation( 
  silhouette_score = anno_barplot(
    df_annot.df$per.cell.sil.width,
    bar_width = 1,
    gp = gpar(col = NA, 
              fontsize = 6 ,
              cex = 0.8 ,
              fill =  sil.fill 
    ),
    border = FALSE,
    height = unit(1.25, "cm") ,
    width = unit(0.5, "cm")
  )
)
  
  
# heatmap annotation
df_annot.df <- df_annot.df %>% dplyr::select(cluster_id)
cluster_colors <- colors[ !is.na(names(colors)) ]
df_annot_cols = HeatmapAnnotation( df = df_annot.df,
                                   col = list(cluster_id=cluster_colors)  , 
                                   show_legend = TRUE , na_col = "gray95",
                                   annotation_name_gp = gpar(fontsize=0.1)
                                  )
  
top10_default_hm <- Heatmap( mat_to_show_default,
                     col = circlize::colorRamp2(range(mat_to_show_default)/2, c("darkslateblue", "yellow")),
                     use_raster = FALSE ,
                     column_split = df_annot.df$cluster_id,
                     heatmap_legend_param = list(color_bar = "continuous") ,	
                     cluster_columns = FALSE ,
                     cluster_rows = FALSE ,
                     show_column_names = FALSE ,
                     name = "Gene Expression (log1p)" ,  
                     row_names_gp = gpar(fontsize = 10) , column_names_gp = gpar(fontsize=8),
                     row_title_side = "left" , row_title_gp = gpar(fontsize = 10, fontface="bold")    
                     )
  
  
  
ht_top10_list = df_annot_cols_cluster_score %v% df_annot_cols %v% top10_default_hm
  
pdf(file.path(figures_path,"top_10_default_heatmap.pdf"), width = 15, height = 23)
p <- draw(ht_top10_list, column_km = 1 , 
          heatmap_legend_side = "right", 
          annotation_legend_side = "bottom" )
dev.off()

p
```

Display a dotplot with the top 5 genes per cluster (using the `data` slot).

```{r, fig.width=24, fig.height=12}
top5_markers <- TE001_markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_log2FC)
  
genes_dp_default <- top5_markers$gene %>% unique()
  
# intialize lists containing dataframes 
Avgs_default <- vector( mode="list", length=length(levels(TE001$seurat_clusters)) )
names(Avgs_default) <- levels(TE001$seurat_clusters)
  
Pct_default <- vector( mode="list", length=length(levels(TE001$seurat_clusters)) )
names(Pct_default) <- levels(TE001$seurat_clusters)
  
for (cluster in levels(TE001$seurat_clusters)){
    
  cells_in_cluster <- TE001$seurat_clusters == cluster
    
  # Avg_default
  Avgs_default[[cluster]] <- TE001[["RNA"]]@data[genes_dp_default, cells_in_cluster] %>%
    rowMeans() %>% 
    as.data.frame() %>% 
    rownames_to_column() %>%
    dplyr::rename(.,"Gene"=rowname,"mean"=.) %>% 
    dplyr::mutate(cluster=cluster)
    
  # Pct
  Pct_default[[cluster]] <- TE001[["RNA"]]@data[genes_dp_default, cells_in_cluster] %>%
    apply(.,1, function(x){ sum(x>0)/length(x)*100 }) %>%
    as.data.frame() %>%
    rownames_to_column() %>%
    dplyr::rename(.,"Gene"=rowname,"Pct"=.) %>%
    dplyr::mutate(cluster=cluster)
    
}
  
# Mean
df_mean.GEx_default <- do.call(rbind, Avgs_default)
rownames(df_mean.GEx_default) <- NULL
# Pct
df_pct.GEx_default <- do.call(rbind, Pct_default)
rownames(df_pct.GEx_default) <- NULL
  
# Summary
summary.GEx_default <- cbind(df_mean.GEx_default[,1:2],df_pct.GEx_default[,2:3]) 
summary.GEx_default$Gene <- factor(summary.GEx_default$Gene, levels=genes_dp_default, ordered = TRUE) 
  
# Printing dotplot for genes
cat("Printing dotplots..\n")
GEx.DGE_DotPlot_default <- ggplot(summary.GEx_default, aes(x=Gene,y=cluster)) +
  geom_point(aes(size = Pct, fill= mean), color="black", shape=21) +
  scale_size("% detected", range = c(0,15), breaks=c(0,25,50,75,100), limits=c(0,100)) +
  scale_fill_gradient2(low="steelblue1", high = "red", mid="white",midpoint=0,
                       limits=range(summary.GEx_default$mean),
                       guide = guide_colorbar(ticks.colour = "black",
                                              frame.colour = "black"),
                       name = "Average Expression\nlog1p(counts)") +
  ylab("Cluster") + xlab("") +
  theme_bw() +
  theme(axis.text.x = element_text(size=10, angle=45, hjust=1, color="black"),
        axis.text.y = element_text(size=12, color="black"),
        axis.title = element_text(size=14) ,
        panel.grid.major = element_blank(), panel.grid.minor = element_blank()
        ) 
  
  
pdf(file.path(figures_path, "GEx-DGE-dotplot_default.pdf") , width = 25 , height = 9)
print(GEx.DGE_DotPlot_default)
dev.off()
  
print(GEx.DGE_DotPlot_default)
```

### 4. Analysis of TE001 with optimized clustering solution

Scale data with `SCTransform` and compute PCA.

```{r}
# new PCA and UMAP will be computed 
TE001@reductions$pca <- NULL
TE001@reductions$umap <- NULL
  
  
TE001 <- SCTransform(object=TE001,
                     assay="RNA",
                     ncells=ncol(TE001),
                     variable.features.n = length(all.genes),
                     do.center = TRUE,
                     do.scale = TRUE,
                     conserve.memory = TRUE)
  
TE001 <- RunPCA(TE001, features=all.genes, verbose=F)
```

Compute clustering solution at the gene expression level by optimizing the silhouette score with `acdc`. Bigger clusters (\>100 cells) are subclustered in a second step for finer grain cell type assignment.

[Clustering (iteration 1)]{.underline}:

```{r}
if (running_clustering == TRUE){
  cat("Computing clustering solution at gene expression..\n")
  control <- list(maxit=1e4,
                  smooth=FALSE,
                  max.time=60*30,
                  temperature=1e9)
    
  TE001 <- SAClustering(S.obj = TE001,
                        assay="SCT",
                        control=control,
                        type.fun = "group.mean.silhouette")
    
  opt_param <- TE001@assays$SCT@misc$SA.history$optim.par # res, NN 
} else if (running_clustering == FALSE){
  cat("Using previously identified optimal solution at gene expression, without recomputing it..\n")
    
  TE001 <- getFinal(S.obj = TE001,
                    assay="SCT",
                    res=opt_param[1],
                    NN=opt_param[2],
                    type.fun="group.mean.silhouette")
    
}

# rename clusters such that levels start from 1 instead of 0, just for consistency
levels(TE001$seurat_clusters) <- as.character(as.numeric(levels(TE001$seurat_clusters)) + 1)
```

Display the silhouette after the first clustering iteration.

```{r}
npcs <- dim( TE001@reductions$pca@cell.embeddings )[2]
  
PCs<-as.data.frame(TE001$pca@cell.embeddings[,1:npcs])
euc_distance<-dist(PCs, method="euclidean")
s <- silhouette( as.integer(TE001$seurat_clusters) , euc_distance )
  
pdf(file.path(figures_path, "step_1_silhouette.pdf") , width = 6 , height = 8)
fviz_silhouette(s,print.summary = FALSE)			
dev.off()

fviz_silhouette(s,print.summary = FALSE)			
```

Show clustering solution after the first iteration on a UMAP embeddings. The first UMAP visualization is the one computed at gene expression level, the second UMAP visualization is the one computed at protein activity.

```{r, fig.width=12, fig.height=6}
npcs <- dim( TE001@reductions$pca@cell.embeddings )[2]
  
TE001 <- RunUMAP(TE001, dims = 1:npcs, verbose=F)
  
pdf(file.path(figures_path,"step_1_clustering_umap.pdf"), width = 12, height = 6)  # Adjust width and height as needed
DimPlot(TE001, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"), reduction="umap", pt.size=1.5)
dev.off()

DimPlot(TE001, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"), reduction="umap", pt.size=1.5)

  
TE001@reductions$umap@cell.embeddings <- as.matrix( TE001@meta.data[c("UMAP_1_scanpy","UMAP_2_scanpy")] )
colnames(TE001@reductions$umap@cell.embeddings) <- c("UMAP_1", "UMAP_2")
  
  
pdf(file.path(figures_path,"step_1_clustering_umap_pa.pdf"), width = 12, height = 6)
DimPlot(TE001, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"), reduction="umap", pt.size=1.5)
dev.off()
  

DimPlot(TE001, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"), reduction="umap", pt.size=1.5)
```

[Clustering (iteration 2)]{.underline}:

```{r}

# Split by cluster and subcluster
TE001_list <- SplitObject(object=TE001, split.by="seurat_clusters")
  
TE001_clusters <- TE001$seurat_clusters %>% unique()
  
cat("Subclustering only clusters with at least 100 cells..\n")
n_cells_per_cluster <- sapply(TE001_list, function(x){dim(x)[2]}) 
TE001_clusters_to_subcluster <- TE001_clusters[n_cells_per_cluster >=100]
  
if (running_clustering == TRUE){
  optim_par_list <- matrix(NA, nrow=length(TE001_clusters_to_subcluster), ncol=2)
  colnames(optim_par_list) <- c("res", "NN")
  rownames(optim_par_list) <- as.character(TE001_clusters_to_subcluster)
  
  cat("Subclustering on a cluster by cluster basis..\n")
  for (k in TE001_clusters_to_subcluster){
    
    TE001_list[[k]] <- SAClustering(S.obj = TE001_list[[k]],
                               assay="SCT",
                               control=control,
                               type.fun = "group.mean.silhouette")
    
  # rename clusters such that levels start from 1 instead of 0
  levels(TE001_list[[k]]@meta.data$seurat_clusters) <- as.character(as.numeric(levels(TE001_list[[k]]@meta.data$seurat_clusters)) + 1)
  # store name for branching
  TE001_list[[k]]@meta.data$seurat_clusters <- paste0(k,"_",TE001_list[[k]]@meta.data$seurat_clusters)
    
  optim_par_list[k,] <-  TE001_list[[k]]@assays$SCT@misc$SA.history$optim.par # res, NN 
  }
} else if (running_clustering == FALSE){
  cat("Subclustering on a cluster by cluster basis by using the previously optimized clustering parameters..\n")
    
  for (k in TE001_clusters_to_subcluster){
      
    opt_res <- optim_par_list[k,"res"]
    opt_NN <- optim_par_list[k,"NN"]
    TE001_list[[k]] <- getFinal(S.obj = TE001_list[[k]],
                                    assay="SCT",
                                    res=opt_res,
                                    NN=opt_NN,
                                    type.fun = "group.mean.silhouette")
      
    levels(TE001_list[[k]]@meta.data$seurat_clusters) <- as.character(as.numeric(levels(TE001_list[[k]]@meta.data$seurat_clusters)) + 1)
    TE001_list[[k]]@meta.data$seurat_clusters <- paste0(k,"_",TE001_list[[k]]@meta.data$seurat_clusters)
 }
}
  
TE001_list[[k]]@meta.data$seurat_clusters <- as.factor(TE001_list[[k]]@meta.data$seurat_clusters)

# remerge Seurat objects into one Seurat object 
TE001_subclustering <- merge(x=TE001_list[[1]],
                                y=TE001_list[2:length(TE001_list)])
```

Display the silhouette after the second clustering iteration.

```{r}
# Re-run PCA to visualize it
TE001_subclustering <- RunPCA(TE001_subclustering, features=all.genes, verbose=F)
  
npcs <- dim( TE001_subclustering@reductions$pca@cell.embeddings )[2]
  
PCs<-as.data.frame(TE001_subclustering$pca@cell.embeddings[,1:npcs])
euc_distance<-dist(PCs, method="euclidean")
  
TE001_subclustering@meta.data$seurat_clusters <- as.factor(TE001_subclustering@meta.data$seurat_clusters)
  
  
s <- silhouette( as.integer(TE001_subclustering$seurat_clusters) , euc_distance )
  
pdf(file.path(figures_path, "step_2_silhouette.pdf") , width = 6 , height = 8)
fviz_silhouette(s,print.summary = FALSE)			
dev.off()
  
fviz_silhouette(s,print.summary = FALSE)			
```

Show clustering solution after the first iteration on a UMAP embeddings. The first UMAP visualization is the one computed at gene expression level, the second UMAP visualization is the one computed at protein activity.

```{r, fig.width=12, fig.height=6}
TE001_subclustering <-  RunUMAP(TE001_subclustering, dims = 1:npcs, verbose=F)

pdf(file.path(figures_path,"step_2_clustering_umap.pdf"), width = 12, height = 6) 
DimPlot(TE001_subclustering, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"), 
          reduction="umap", pt.size=1.5 )
dev.off()
  
DimPlot(TE001_subclustering, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"), 
          reduction="umap", pt.size=1.5 )
  
TE001_subclustering@reductions$umap@cell.embeddings <- as.matrix( TE001_subclustering@meta.data[c("UMAP_1_scanpy","UMAP_2_scanpy")] )
colnames(TE001_subclustering@reductions$umap@cell.embeddings) <- c("UMAP_1", "UMAP_2")
  
pdf(file.path(figures_path,"step_2_clustering_umap_pa.pdf"), width = 12, height = 6)
DimPlot(TE001_subclustering, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"), 
        reduction="umap", pt.size=1.5 )
dev.off()
  
DimPlot(TE001_subclustering, group.by=c("seurat_clusters", "iter_cluster_id_with_paneth"), 
        reduction="umap", pt.size=1.5 )
```

Display cytotrace score on a UMAP.

```{r, fig.width=6, fig.height=6}

pdf(file.path(figures_path,"step_2_clustering_umap_pa_cytotrace.pdf"), width = 6, height = 6)
FeaturePlot(TE001_subclustering, features = "cytotrace_te001", pt.size=1.5) & scale_color_viridis_c()
dev.off()

FeaturePlot(TE001_subclustering, features = "cytotrace_te001", pt.size=1.5) & scale_color_viridis_c()

```

Perform differential gene expression analysis on the identified clusters after the solution optimized in 2 steps.

```{r}
# Perform differential gene expression analysis
cat("Perform differential gene expression analysis on the identified clusters..\n")
Idents(TE001_subclustering) <- "seurat_clusters"
  
# Use RNA slot for calculation of DE genes
TE001_subclustering <- NormalizeData(TE001_subclustering, assay="RNA", normalization.method = "LogNormalize")
  
# FindAllMarkers
TE001_subclustering_markers <- FindAllMarkers(TE001_subclustering, 
                                              assay = "RNA",
                                              slot="data",
                                              test.use = "wilcox", 
                                              only.pos = TRUE, 
                                              min.pct = 0.25)
  
top10 <- TE001_subclustering_markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
```

Generate a heatmap showing the top 10 differentially expressed genes on a per-cluster basis

```{r, fig.width=18, fig.height=24}
genes <- top10$gene
mat_to_show <- as.matrix(TE001_subclustering@assays$SCT@scale.data[genes,])
  
Clusters <- TE001_subclustering$seurat_clusters
colors <- rainbow(nlevels(Clusters)) 
names(colors) <- levels(Clusters)
  
# prepare df_annot.df 
per.cell.sil.width <- s[,"sil_width"]
names(per.cell.sil.width) <- colnames(TE001_subclustering)
  
df_annot.df <- TE001_subclustering@meta.data %>%
  dplyr::select(cluster_id=seurat_clusters, cytotrace_score = cytotrace_te001) %>%
  tibble::rownames_to_column(.,var="cell_id")
df_annot.df$cluster_id <- factor(as.numeric(df_annot.df$cluster_id))
  
x <- as.data.frame(per.cell.sil.width) %>%
  tibble::rownames_to_column(.,var="sample_id")
   
df_annot.df <- left_join(df_annot.df,x,by=c("cell_id"="sample_id"))
  
df_annot.df <- merge(df_annot.df, TE001_subclustering@meta.data[, c("cell_id", "seurat_clusters")], by = "cell_id", all.x = TRUE) # rename clusters as a tree-like structure
df_annot.df <- df_annot.df[,-2] %>% dplyr::rename(cluster_id=seurat_clusters)

sample_ordering_to_print <- rev( order( df_annot.df$per.cell.sil.width ) )
  
df_annot.df <- df_annot.df[sample_ordering_to_print,]
  
mat_to_show <- mat_to_show[,match(df_annot.df$cell_id,colnames(mat_to_show))]
  
stopifnot(identical( df_annot.df$cell_id, colnames(mat_to_show) ))
  
# the silhouette score
sil.fill <- rep(NA, nrow(df_annot.df))
unique_clusters <- unique(df_annot.df$cluster_id)
n_colors <- length(colors)
  
color_index <- 1
for (i in seq_along(unique_clusters)) {
  cluster <- unique_clusters[i]
  sil.fill[df_annot.df$cluster_id == cluster] <- colors[names(colors) == cluster]
  color_index <- i + 1  # Use modulo to cycle through colors
}
  
df_annot_cols_cluster_score <- HeatmapAnnotation( 
  silhouette_score = anno_barplot(
    df_annot.df$per.cell.sil.width,
    bar_width = 1,
    gp = gpar(col = NA, 
              fontsize = 6 ,
              cex = 0.8 ,
              fill =  sil.fill 
    ),
    border = FALSE,
    height = unit(1.25, "cm") ,
    width = unit(0.5, "cm")
  )
)
  
# heatmap annotation
df_annot.df <- df_annot.df %>% dplyr::select(cluster_id)
cluster_colors <- colors[ !is.na(names(colors)) ]
df_annot_cols = HeatmapAnnotation( df = df_annot.df,
                                   col = list(cluster_id=cluster_colors)  , 
                                   show_legend = TRUE , na_col = "gray95",
                                   annotation_name_gp = gpar(fontsize=0.1)
)
  
top10_hm <- Heatmap( mat_to_show,
                   col = circlize::colorRamp2(range(mat_to_show_default)/2, c("darkslateblue", "yellow")),
                   use_raster = FALSE ,
                   column_split = df_annot.df$cluster_id,
                   heatmap_legend_param = list(color_bar = "continuous") ,	
                   cluster_columns = FALSE ,
                   cluster_rows = FALSE ,
                   show_column_names = FALSE ,
                   name = "Gene Expression (log1p)" ,  
                   row_names_gp = gpar(fontsize = 10) , column_names_gp = gpar(fontsize = 8),
                  row_title_side = "left" , row_title_gp = gpar(fontsize = 10, fontface = "bold")
  )
  
ht_list = df_annot_cols_cluster_score %v% df_annot_cols %v% top10_hm
  
pdf(file.path(figures_path,"top_10_heatmap.pdf"), width = 10, height = 12)
p <- draw(ht_list, column_km = 1 , 
          heatmap_legend_side = "right", 
          annotation_legend_side = "bottom" )
dev.off()  

p
```

Display a dotplot with the top 5 genes per cluster (using the `data` slot).

```{r, fig.width=22, fig.height=9}
# DotPlot
top5 <- TE001_subclustering_markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_log2FC)
  
genes_dp <- top5$gene %>% unique()
  
# intialize lists containing dataframes 
Avgs <- vector( mode="list", length=length(levels(TE001_subclustering$seurat_clusters)) )
names(Avgs) <- levels(TE001_subclustering$seurat_clusters)
  
Pct <- vector( mode="list", length=length(levels(TE001_subclustering$seurat_clusters)) )
names(Pct) <- levels(TE001_subclustering$seurat_clusters)
  
for (cluster in levels(TE001_subclustering$seurat_clusters)){
  
  cells_in_subcluster <- TE001_subclustering$seurat_clusters == cluster
    
  # Avg 
  Avgs[[cluster]] <- TE001_subclustering[["RNA"]]@data[genes_dp, cells_in_subcluster] %>%
    rowMeans() %>% 
    as.data.frame() %>% 
    rownames_to_column() %>%
    dplyr::rename(.,"Gene"=rowname,"mean"=.) %>% 
    dplyr::mutate(cluster=cluster)
      
  # Pct
  Pct[[cluster]] <- TE001_subclustering[["RNA"]]@data[genes_dp, cells_in_subcluster] %>%
    apply(.,1, function(x){ sum(x>0)/length(x)*100 }) %>%
    as.data.frame() %>%
    rownames_to_column() %>%
    dplyr::rename(.,"Gene"=rowname,"Pct"=.) %>%
    dplyr::mutate(cluster=cluster)
}
  
# Mean
df_mean.GEx <- do.call(rbind, Avgs)
  rownames(df_mean.GEx) <- NULL
# Pct
df_pct.GEx <- do.call(rbind, Pct)
rownames(df_pct.GEx) <- NULL
  
# Summary
summary.GEx <- cbind(df_mean.GEx[,1:2],df_pct.GEx[,2:3]) 
summary.GEx$Gene <- factor(summary.GEx$Gene, levels=genes_dp, ordered = TRUE) 
  
# Printing dotplot for genes
cat("Printing dotplots..\n")
GEx.DGE_DotPlot <- ggplot(summary.GEx, aes(x=Gene,y=cluster)) +
  geom_point(aes(size = Pct, fill= mean), color="black", shape=21) +
  scale_size("% detected", range = c(0,15), breaks=c(0,25,50,75,100), limits=c(0,100)) +
  scale_fill_gradient2(low="steelblue1", high = "red", mid="white",midpoint=0,
                       limits=range(summary.GEx$mean),
                       guide = guide_colorbar(ticks.colour = "black",
                                              frame.colour = "black"),
                       name = "Average Expression\nlog1p(counts)") +
  ylab("Cluster") + xlab("") +
  theme_bw() +
  theme(axis.text.x = element_text(size=10, angle=45, hjust=1, color="black"),
        axis.text.y = element_text(size=12, color="black"),
        axis.title = element_text(size=14) ,
        panel.grid.major = element_blank(), panel.grid.minor = element_blank()
  ) 
  
  
pdf(file.path(figures_path, "GEx-DGE-dotplot.pdf") , width = 22 , height = 9)
print(GEx.DGE_DotPlot)
dev.off()
  
print(GEx.DGE_DotPlot)
```

Save Seurat Object with clustering solution as an `.rds` file to the `DataFolder` location.

```{r}
saveRDS(object=TE001_subclustering, file=file.path(savingsFolder, "TE001_iter_subclustering_gene_expr.rds"))
```

```{r}
sessionInfo()
```
